What mattered
Post-training is a system of custom data, reward shaping, performance work, infrastructure, evaluation, and packaging rather than a generic fine-tuning screen.
What changed locally
PostTrainLLM was reframed as a Mac-local specialist factory organized around target, data, post-training, evaluation, packaging, and reporting.
Evidence limits
The positioning was adopted, not Baseten's hosted infrastructure or scale assumptions.
Retained disposition
Keep factory documentation centered on the complete evidence loop and one-Mac constraints.
The action is retained as study context rather than an active backlog item. Any implementation, download, training run, or benchmark requires a fresh scoped question under the repository's experiment gate.
How to read this dossier
This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method.
Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.
To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.
Source provenance
The normalized record comes from docs/studies/registry.json. The links below are the tracked evidence and explanatory sources preserved with the record.